Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/02—Arrangements for optimising operational condition
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/04—Architecture, e.g. interconnection topology
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/08—Learning methods
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/08—Configuration management of networks or network elements
  • H04L41/0803—Configuration setting
  • H04L41/0806—Configuration setting for initial configuration or provisioning, e.g. plug-and-play
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/08—Configuration management of networks or network elements
  • H04L41/085—Retrieval of network configuration; Tracking network configuration history
  • H04L41/0853—Retrieval of network configuration; Tracking network configuration history by actively collecting configuration information or by backing up configuration information
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/08—Testing, supervising or monitoring using real traffic
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
  • H04L41/14—Network analysis or design
  • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L43/00—Arrangements for monitoring or testing data switching networks
  • H04L43/06—Generation of reports
  • H04L43/065—Generation of reports related to network devices
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L43/00—Arrangements for monitoring or testing data switching networks
  • H04L43/16—Threshold monitoring
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

• an artificial intelligence (artificial intelligence, AI) algorithm is expected to be used to break through performance bottlenecks caused by conventional modular system modeling and model approximation in typical fields such as channel estimation, pilot detection, signal equalization, and user scheduling.
• AI artificial intelligence
• the AI algorithm can improve network performance in aspects of channel state information (channel state information, CSI) feedback enhancement, beam management, positioning accuracy enhancement, energy saving, mobility optimization, load balancing, and the like.
• An AI model may be usually deployed on a base station side or a terminal side. How to improve network performance when the AI model is enabled is an urgent problem to be resolved.
• the terminal device may determine, based on the first condition threshold, whether to enable the AI function. For example, when the AI inference result of the terminal device meets the first condition threshold, the terminal device enables the AI function. The terminal device determines, based on the AI inference result, whether to enable the AI function. This better meets a communication status of the terminal device, and a determining result is more accurate.
• the first condition threshold includes either an AI inference result confidence threshold or a deviation threshold between the AI inference result and actual measurement information.
• the configuration information includes area information
• the area information indicates an area in which the terminal device is allowed to enable the AI function.
• the determining, based on the configuration information, whether to enable the AI function includes: enabling the AI function in the area indicated by the area information.
• the terminal device may determine, based on the area information, whether to enable the AI function. For example, the terminal device enables the AI function in the area indicated by the area information, so that the AI function can be enabled more accurately, and computing resources of the terminal can be saved.
• the communication method when it is determined, based on the configuration information, to enable the AI function, the communication method further includes: enabling an AI model for implementing the AI function; receiving a measurement configuration from the network device; obtaining the AI inference result based on the measurement configuration and the AI model; and sending a measurement report to the network device, where the measurement report includes the AI inference result.
• the measurement report further includes at least one of second indication information, third indication information, and fourth indication information.
• the second indication information indicates that the AI inference result is a result obtained through inference based on the AI model
• the third indication information indicates whether the AI inference result is consistent with the actual measurement information
• the fourth indication information indicates deployment node information and collaboration level information of the AI model.
• the measurement report may not only include the AI inference result, but also include more of the foregoing information (for example, the second indication information, the third indication information, and the fourth indication information), so that the network device determines the cause of the network performance deterioration based on more information. This can improve accuracy of determining, by the network device, the cause of the network performance deterioration.
• the measurement report further includes one or more of indication information for suggesting whether to disable the AI function and key performance indicator (key performance indicator, KPI) feedback information.
• collaboration levels may be classified into three types: a level 0 AI collaboration function (level 0 for short), a level 1 AI collaboration function (level 1 for short), and a level 2 AI collaboration function (level 2 for short).
• the level 0 is also referred to as no collaboration (No collaboration).
• No collaboration an AI model of the network device is completely invisible to the terminal device. Training and inference of the AI model are completed inside the network device, and information about the model or information about a model instance is not sent to the terminal device over an air interface.
• the level 2 is also referred to as signaling-based collaboration with model transfer (signaling-based collaboration with model transfer).
• the AI model is deployed in each of the network device and the terminal device, and both the network device and the terminal device can perform inference.
• the information about the AI model can be exchanged over the air interface.
• a difference between the level 2 and the level 1 includes that in the level 2, the information about the AI model can be transmitted over the air interface.
• the level 0/1/2 is merely an example.
• the level 0 may alternatively be represented by a level x
• the level 1 may alternatively be represented by a level y
• the level 2 may alternatively be represented by a level z. This is not limited in this application.
• AI use case is also referred to as an AI application scenario or the AI function.
• AI use cases include but are not limited to: channel state information (channel status information, CSI) feedback enhancement (CSI feedback enhancement), beam management enhancement (beam management enhancement), positioning enhancement (positioning accuracy enhancement), network energy saving (network energy saving), load balancing (load balancing), and mobility optimization (mobility optimization). Descriptions are separately provided below.
• one AI function may include a plurality of AI sub-functions.
• the load balancing enables loads to be evenly distributed between cells and between areas in the cell, or transfers some traffic from a congested cell, or allows a user to perform traffic distribution in a cell, a carrier, or an access mode, to improve network performance.
• Load balancing performance is improved based on the AI model. For example, various measurements, feedback, historical data, and the like of the terminal device and a network node are input to the AI model to improve the load balancing performance, so that higher-quality user experience can be provided, and a system capacity can be improved.
• the mobility management is a solution that ensures service continuity during movement of the terminal device by minimizing a call drop, a radio link failure (radio link failure, RLF), unnecessary handover, and a ping-pong effect.
• AI can enhance the mobility management, for example, reduce a probability of an unexpected event, predict a position, mobility, or performance of the terminal device, and guide traffic.
• the terminal device In a connected (connected) state, the terminal device establishes an RRC connection to the network device for data transmission.
• the terminal device In an inactive (inactive) state, the terminal device first enters a connected state, then the network device releases an RRC connection, and the network device and the terminal device store a context. If the terminal device needs to enter the connected state from the inactive state, the terminal device needs to initiate an RRC connection resume process. In comparison with an RRC establishment process, the RRC resume process has a shorter delay and fewer signaling overheads.
• FIG. 4 is an interaction diagram of a communication method according to an embodiment of this application. As shown in FIG. 4 , the communication method may include S401 and S402.
• the network device may manage whether to enable the AI function on the terminal device side, to improve management and control of a network side on network performance, and reduce impact of enabling the AI function on the terminal device side on the network performance.
• the terminal device determines, based on the configuration information, whether to enable the AI function.
• the terminal device enables an AI model for implementing the AI function.
• the terminal device obtains an AI inference result based on the measurement configuration and the AI model.
• Beam management enhancement is used as an example for description.
• the terminal device receives the measurement configuration from the network device as a training input (for example, the SSB beam sweeping).
• Model training is completed inside the terminal device, and the terminal device may generate k (where k is a positive integer) pieces of optimal beam information based on the AI inference result.
• the terminal device may feed back, to the network device, information such as the k pieces of optimal beam information and an optimal CSI-RS identifier that are based on the AI inference result.
• CSI enhancement is used as an example for description.
• the terminal device uses the measurement configuration, for example, the CSI-RS received from the network device as a training input.
• Model training is completed inside the terminal device, and information such as a CQI, a precoding matrix indicator (precoding matrix indicator, PMI), and a CSI-RS resource indicator (CSI-RS resource indicator, CRI) that are optimized based on the AI inference result is obtained.
• the terminal device sends a measurement report to the network device, where the measurement report includes the AI inference result.
• the measurement report may further include any one or more of the following:
• the measurement report may further include one or more of a type of the AI model (for example, the original AI model or the trained and optimized AI model), parameter adjustment information of the AI model (for example, parameter adjustment information of the trained and optimized AI model relative to the original AI model), indication information for suggesting whether to disable the AI function, and KPI feedback information.
• the terminal device may independently determine whether the AI inference result is consistent with the actual measurement result, or whether a deviation between the AI inference result and the actual measurement result exceeds a deviation threshold, and may report the indication information for suggesting whether to disable the AI function.
• the KPI feedback information may be information obtained through actual measurement by the terminal device.
• the KPI feedback information may include a downlink rate and the like.
• the KPI feedback information may include prediction accuracy of K optimal beams and the like.
• the KPI feedback information may include indication information indicating time of arrival (TOA) of a channel of a LOS path and the like, or include indication information indicating whether a LOS/NLOS state obtained through AI model inference is accurate and the like.
• TOA time of arrival
• the terminal device does not perform measurement, and selects the recovery beam based on the AI inference result to perform BFR. If the BFR fails, the terminal device reports the measurement report, and indicates, to the network device, that the recovery beam selected by the terminal device is obtained based on the AI inference result. In this way, the network device can determine that a cause of the beam failure recovery is that the selected beam is not applicable to the beam failure recovery due to the improper AI model on the terminal device side. Therefore, the network device may indicate the terminal device to optimize or disable the AI model on the terminal device side.
• the network device may determine, based on the measurement report, whether a network exception or network performance deterioration is caused by an improper configuration on a network side or caused by an inaccurate AI inference result on the terminal device side.
• the network device may determine a cause of the network performance deterioration, and correspondingly use a policy to resolve a network performance deterioration problem, for example, adjust and resend the measurement configuration to the terminal device, or send, to the terminal device, the indication information indicating the terminal device to disable the AI function, to improve management and control of the network side on the network performance, and reduce impact of enabling the AI function on the terminal device side on the network performance.
• the CU-CP 2 receives the configuration information sent by the core network device, and generates air interface RRC signaling including the configuration information.
• the network device may independently determine the configuration information for the terminal device, the CU-CP 1 determines the configuration information based on, for example, performance and a capability of the terminal device, and the CU-CP 2 determines air interface RRC signaling, and sends the configuration information to the terminal device by using the RRC signaling.
• the configuration information and an operation performed after the terminal device receives the configuration information refer to steps S401 and S402 in the embodiment in FIG. 4 and steps S502 to S506 in the embodiment in FIG. 5 . Details are not described again.
• FIG. 6 is an interaction diagram of still another communication method according to an embodiment of this application.
• the communication method may include S601 to S605.
• S605 is an optional step.
• the terminal device enables the AI model for implementing the AI function.
• steps S601 to S605 For specific descriptions of steps S601 to S605, refer to steps S504 to S508. Details are not described again.
• FIG. 7 is a diagram of a structure of a communication apparatus according to an embodiment of this application.
• the communication apparatus may be a terminal device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the terminal device.
• the communication apparatus 700 includes at least a receiving unit 701, a processing unit 702, and a sending unit 703.
• the receiving unit 701 is configured to receive configuration information of an AI function from a network device.
• the AI function includes at least one type of AI function, and different types of AI functions correspond to different degrees of collaboration between the network device and the terminal device.
• the configuration information includes first indication information, and the first indication information indicates whether the terminal device enables the AI function.
• the processing unit 702 determines, based on the configuration information, whether to enable the AI function, and is specifically configured to: determine, based on the first indication information, whether to enable the AI function.
• the first condition threshold includes either an AI inference result confidence threshold or a deviation threshold between the AI inference result and actual measurement information.
• the configuration information further includes area information, and the area information indicates an area in which the terminal device is allowed to enable the AI function.
• the processing unit 702 determines, based on the configuration information, whether to enable the AI function, and is specifically configured to: when the first indication information indicates the terminal device to enable the AI function, determine to enable the AI function in the area indicated by the area information.
• the receiving unit 701 is further configured to receive a measurement configuration from the network device.
• the processing unit 702 is further configured to obtain the AI inference result based on the measurement configuration and the AI model.
• the measurement report further includes at least one of second indication information, third indication information, and fourth indication information, where the second indication information indicates that the AI inference result is a result obtained through inference based on the AI model, the third indication information indicates whether the AI inference result is consistent with the actual measurement information, and the fourth indication information indicates deployment node information and collaboration level information of the AI model.
• the measurement report further includes one or more of indication information for suggesting whether to disable the AI function and KPI feedback information.
• the processing unit 702 is further configured to disable the AI function based on the fifth indication information.
• receiving unit 701, the processing unit 702, and the sending unit 703, refer directly to related descriptions of the terminal device in the method embodiments shown in FIG. 4 to FIG. 6 . Details are not described herein again.
• FIG. 8 is a diagram of a structure of another communication apparatus according to an embodiment of this application.
• the communication apparatus may be a network device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the network device.
• the communication apparatus 800 includes at least an obtaining unit 801, a sending unit 802, and a receiving unit 803.
• the sending unit 802 is configured to send configuration information of the AI function to the terminal device, where the configuration information indicates whether the terminal device enables the AI function.
• the configuration information includes first indication information, and the first indication information indicates whether the terminal device enables the AI function.
• the configuration information includes area information, and the area information indicates an area in which the terminal device is allowed to enable the AI function.
• the sending unit 802 is further configured to send a measurement configuration to the terminal device.
• the measurement report further includes at least one of second indication information, third indication information, and fourth indication information, where the second indication information indicates that the AI inference result is a result obtained through inference based on the AI model, the third indication information indicates whether the AI inference result is consistent with the actual measurement information, and the fourth indication information indicates deployment node information and collaboration level information of the AI model.
• the measurement report further includes one or more of indication information for suggesting whether to disable the AI function and KPI feedback information.
• the sending unit 802 is further configured to send fifth indication information to the terminal device, where the fifth indication information indicates the terminal device to disable the AI function.
• the obtaining unit 801, the sending unit 802, and the receiving unit 803 refer directly to related descriptions of the network device in the method embodiments shown in FIG. 4 to FIG. 6 . Details are not described herein again.
• FIG. 9 is a diagram of a structure of still another communication apparatus according to an embodiment of this application.
• the communication apparatus may be a terminal device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the terminal device.
• the communication apparatus 900 includes at least a processing unit 901, a receiving unit 902, and a sending unit 903.
• the receiving unit is configured to receive a measurement configuration from a network device.
• the sending unit is configured to send a measurement report to the network device, where the measurement report includes the AI inference result.
• the measurement report further includes at least one of second indication information, third indication information, and fourth indication information, where the second indication information indicates that the AI inference result is a result obtained through inference based on the AI model, the third indication information indicates whether the AI inference result is consistent with actual measurement information, and the fourth indication information indicates deployment node information and collaboration level information of the AI model.
• the processing unit 901 is further configured to disable the AI function based on the fifth indication information.
• processing unit 901, the receiving unit 902, and the sending unit 903 refer directly to related descriptions of the terminal device in the method embodiments shown in FIG. 4 to FIG. 6 . Details are not described herein again.
• FIG. 10 is a diagram of a structure of still another communication apparatus according to an embodiment of this application.
• the communication apparatus may be a network device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the network device.
• the communication apparatus 1000 includes at least a sending unit 1001 and a receiving unit 1002.
• the sending unit 1001 is configured to send a measurement configuration to a terminal device.
• the receiving unit 1002 is configured to receive a measurement report from the terminal device, where the measurement report includes an AI inference result, and the AI inference result is obtained based on the measurement configuration and an AI model for implementing an AI function.
• the measurement report further includes at least one of second indication information, third indication information, and fourth indication information, where the second indication information indicates that the AI inference result is a result obtained through inference based on the AI model, the third indication information indicates whether the AI inference result is consistent with actual measurement information, and the fourth indication information indicates deployment node information and collaboration level information of the AI model.
• FIG. 11 is a diagram of a structure of still another communication apparatus according to an embodiment of this application.
• the apparatus 1100 may include one or more processors 1101, and the processor 1101 may also be referred to as a processing unit, and may implement a specific control function.
• the processor 1101 may be a general-purpose processor, a dedicated processor, or the like.
• the processor 1101 may be a baseband processor or a central processing unit.
• the baseband processor may be configured to process a communication protocol and communication data.
• the central processing unit may be configured to: control a communication apparatus (for example, a base station, a baseband chip, a terminal, a terminal chip, a DU, or a CU), execute a software program, and process data of the software program.
• the processor 1101 may include a transceiver unit configured to implement receiving and sending functions.
• the transceiver unit may be a transceiver circuit, an interface, an interface circuit, or a communication interface.
• the transceiver circuit, the interface, or the interface circuit configured to implement the receiving and sending functions may be separated, or may be integrated together.
• the transceiver circuit, the interface, or the interface circuit may be configured to read and write code/data.
• the transceiver circuit, the interface, or the interface circuit may be configured to transmit or transfer a signal.
• the apparatus 1100 may include a circuit, and the circuit may implement the sending, receiving, or communication function in the foregoing method embodiments.
• the apparatus 1100 may include one or more memories 1102.
• the memory 1102 may store instructions 1104 and/or data, and the instructions 1104 and/or the data may be run on the processor, to enable the apparatus 1100 to perform the methods described in the foregoing method embodiments.
• the memory may further store data.
• the processor may also store instructions and/or data.
• the processor and the memory may be separately disposed, or may be integrated together. For example, the correspondence described in the foregoing method embodiments may be stored in the memory or stored in the processor.
• the apparatus 1100 in this embodiment of this application may be configured to perform the methods described in FIG. 4 to FIG. 6 in embodiments of this application.
• the communication apparatus 1100 may be a terminal device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the terminal device.
• the processor 1101 is configured to: perform the operations performed by the processing unit 702 in the foregoing embodiment, or perform the operations performed by the processing unit 901 in the foregoing embodiment
• the transceiver 1105 is configured to: perform the operations performed by the receiving unit 701 and the sending unit 703 in the foregoing embodiment, or perform the operations performed by the receiving unit 902 and the sending unit 903 in the foregoing embodiment
• the transceiver 1105 is further configured to send information to a communication apparatus other than the communication apparatus.
• the terminal device or the apparatus in the terminal device may be further configured to perform various methods performed by the terminal device in the method embodiments in FIG. 4 to FIG. 6 . Details are not described again.
• the communication apparatus 1100 may be a network device, or may be an apparatus (for example, a chip, a chip system, or a circuit) in the network device.
• the processor 1101 is configured to perform the operations performed by the obtaining unit 801 in the foregoing embodiment
• the transceiver 1105 is configured to: perform the operations performed by the sending unit 802 and the receiving unit 803 in the foregoing embodiment, or perform the operations performed by the sending unit 1001 and the receiving unit 1002 in the foregoing embodiment
• the transceiver 1105 is further configured to receive information from a communication apparatus other than the communication apparatus.
• the network device or the apparatus in the network device may be further configured to perform various methods performed by the network device in the method embodiments in FIG. 4 to FIG. 6 . Details are not described again.
• the processor and the transceiver that are described in this application may be implemented on an integrated circuit (integrated circuit, IC), an analog IC, a radio frequency integrated circuit (radio frequency interface chip, RFIC), a hybrid signal IC, an application-specific integrated circuit (application-specific integrated circuit, ASIC), a printed circuit board (printed circuit board, PCB), an electronic device, or the like.
• integrated circuit integrated circuit, IC
• analog IC analog IC
• RFIC radio frequency integrated circuit
• RFIC radio frequency interface chip
• hybrid signal IC an application-specific integrated circuit
• ASIC application-specific integrated circuit
• PCB printed circuit board
• an electronic device or the like.
• the processor and the transceiver may alternatively be manufactured by using various IC technologies, for example, a complementary metal oxide semiconductor (complementary metal oxide semiconductor, CMOS), an N-type metal oxide semiconductor (nMetal-oxide-semiconductor, NMOS), a P-type metal oxide semiconductor (positive channel metal oxide semiconductor, PMOS), a bipolar junction transistor (Bipolar Junction Transistor, BJT), a bipolar CMOS (BiCMOS), silicon germanium (SiGe), and gallium arsenide (GaAs).
• CMOS complementary metal oxide semiconductor
• NMOS N-type metal oxide semiconductor
• NMOS N-type metal oxide semiconductor
• P-type metal oxide semiconductor positive channel metal oxide semiconductor
• BJT bipolar junction transistor
• BiCMOS bipolar CMOS
• SiGe silicon germanium
• GaAs gallium arsenide
• the apparatus described in the foregoing embodiment may be a first communication device or a second communication device.
• a range of the apparatus described in this application is not limited thereto, and a structure of the apparatus may not be limited to FIG. 11 .
• the apparatus may be an independent device, or may be a part of a large device.
• the apparatus may be:
• the processor may read a software program in a storage unit, parse and execute instructions of the software program, and process the data of the software program.
• the processor performs baseband processing on the to-be-sent data, and outputs a baseband signal to the radio frequency circuit.
• the radio frequency circuit processes the baseband signal to obtain a radio frequency signal, and sends the radio frequency signal to the outside in the form of an electromagnetic wave through the antenna.
• the radio frequency circuit receives the radio frequency signal through the antenna, further converts the radio frequency signal into a baseband signal, and outputs the baseband signal to the processor.
• the processor converts the baseband signal into data, and processes the data.
• the processor may include a baseband processor and a central processing unit.
• the baseband processor is mainly configured to process the communication protocol and the communication data
• the central processing unit is mainly configured to: control the entire terminal, execute the software program, and process the data of the software program.
• the processor in FIG. 12 integrates functions of the baseband processor and the central processing unit.
• the baseband processor and the central processing unit each may be an independent processor, and are interconnected by using a technology such as a bus.
• the terminal may include a plurality of baseband processors to adapt to different network standards, and the terminal may include a plurality of central processing units to enhance a processing capability of the terminal.
• the baseband processor may also be expressed as a baseband processing circuit or a baseband processing chip.
• the central processing unit may also be expressed as a central processing circuit or a central processing chip.
• a function of processing the communication protocol and the communication data may be built in the processor, or may be stored in the storage unit in a form of a software program.
• the processor executes the software program to implement a baseband processing function.
• the antenna and the control circuit that have a transceiver function may be considered as a transceiver unit 1201 of the terminal device 1200, and the processor that has a processing function may be considered as a processing unit 1202 of the terminal device 1200.
• the terminal device 1200 includes the transceiver unit 1201 and the processing unit 1202.
• the transceiver unit may also be referred to as a transceiver, a transceiver machine, a transceiver apparatus, or the like.
• a component that is in the transceiver unit 1201 and that is configured to implement a receiving function may be considered as a receiving unit
• a component that is in the transceiver unit 1201 and that is configured to implement a sending function may be considered as a sending unit.
• the transceiver unit 1201 includes the receiving unit and the sending unit.
• the receiving unit may also be referred to as a receiver machine, a receiver, a receiver circuit, or the like.
• the sending unit may be referred to as a transmitter machine, a transmitter, a transmitter circuit, or the like.
• the receiving unit and the sending unit may be one integrated unit, or may be a plurality of independent units.
• the receiving unit and the sending unit may be in one geographical position, or may be distributed in a plurality of geographical positions.
• the processing unit 1202 is configured to: perform the operations performed by the processing unit 702 in the foregoing embodiment, or perform the operations performed by the processing unit 901 in the foregoing embodiment
• the transceiver unit 1201 is configured to: perform the operations performed by the receiving unit 701 and the sending unit 703 in the foregoing embodiment, or perform the operations performed by the receiving unit 902 and the sending unit 903 in the foregoing embodiment.
• the terminal device 1200 may be further configured to perform various methods performed by the terminal device in the method embodiments in FIG. 4 to FIG. 6 . Details are not described again.
• An embodiment of this application further provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program.
• the program is executed by a processor, a procedure related to the terminal device in the transmission mode determining method provided in the foregoing method embodiments may be implemented.
• An embodiment of this application further provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program.
• the program is executed by a processor, a procedure related to the network device in the transmission mode determining method provided in the foregoing method embodiments may be implemented.
• An embodiment of this application further provides a computer program product.
• the computer program product runs on a computer or a processor, the computer or the processor is enabled to perform one or more steps in any one of the foregoing transmission mode determining methods.
• the component modules may be stored in the computer-readable storage medium.
• An embodiment of this application further provides a chip system, including at least one processor and a communication interface.
• the communication interface and the at least one processor are interconnected through a line, and the at least one processor is configured to run a computer program or instructions, to perform some or all of the steps recorded in any one of the method embodiments corresponding to FIG. 4 to FIG. 6 .
• the chip system may include a chip, or may include a chip and another discrete component.
• An embodiment of this application further discloses a communication system, and the system includes a terminal device and a network device.
• the system includes a terminal device and a network device.
• the transmission mode determining methods shown in FIG. 4 to FIG. 6 refer to the transmission mode determining methods shown in FIG. 4 to FIG. 6 .
• the memory mentioned in embodiments of this application may be a volatile memory or a non-volatile memory, or may include both the volatile memory and the non-volatile memory.
• the non-volatile memory may be a hard disk drive (hard disk drive, HDD), a solid-state drive (solid-state drive, SSD), a read-only memory (read-only memory, ROM), a programmable read-only memory (programmable ROM, PROM), an erasable programmable read-only memory (erasable PROM, EPROM), an electrically erasable programmable read-only memory (electrically EPROM, EEPROM), or a flash memory.
• the volatile memory may be a random access memory (random access memory, RAM), and is used as an external cache.
• RAM random access memory
• many forms of RAMs may be used, for example, a static random access memory (static RAM, SRAM), a dynamic random access memory (dynamic RAM, DRAM), a synchronous dynamic random access memory (synchronous DRAM, SDRAM), a double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), an enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), a synchlink dynamic random access memory (synchlink DRAM, SLDRAM), and a direct rambus random access memory (direct rambus RAM, DR RAM).
• static random access memory static random access memory
• DRAM dynamic random access memory
• DRAM synchronous dynamic random access memory
• double data rate SDRAM double data rate SDRAM
• DDR SDRAM double data rate SDRAM
• ESDRAM enhanced synchronous dynamic random access memory
• synchlink dynamic random access memory synchlink dynamic random access memory
• the memory is any other medium that can carry or store expected program code in a form of an instruction structure or a data structure and that can be accessed by a computer, but is not limited thereto.
• the memory in embodiments of this application may alternatively be a circuit or any other apparatus that can implement a storage function, and is configured to store program instructions and/or data.
• the processor mentioned in embodiments of this application may be a central processing unit (central processing unit, CPU), or may be another general-purpose processor, a digital signal processor (digital signal processor, DSP), an application-specific integrated circuit (application-specific integrated circuit, ASIC), a field programmable gate array (field programmable gate array, FPGA) or another programmable logic device, a discrete gate or a transistor logic device, a discrete hardware component, or the like.
• the general-purpose processor may be a microprocessor, or the processor may be any conventional processor or the like.
• the processor is a general-purpose processor, a DSP, an ASIC, an FPGA or another programmable logic device, a discrete gate or a transistor logic device, or a discrete hardware component
• the memory is integrated into the processor.
• sequence numbers of the foregoing processes do not mean execution sequences in various embodiments of this application.
• the execution sequences of the processes should be determined based on functions and internal logic of the processes, and should not be construed as any limitation on the implementation processes of embodiments of this application.
• the disclosed system, apparatus, and method may be implemented in other manners.
• the described apparatus embodiments are merely examples.
• division into the units is merely logical function division and there may be another division manner in actual implementation.
• a plurality of units or components may be combined or integrated into another system, or some features may be ignored or not performed.
• the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented through some interfaces.
• the indirect couplings or communication connections between the apparatuses or units may be implemented in an electronic form, a mechanical form, or another form.
• a sequence of the steps of the methods in embodiments of this application may be adjusted, combined, and deleted based on an actual requirement.
• modules/units in the apparatuses in embodiments of this application may be combined, divided, and deleted based on an actual requirement.

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